For model-free methods, we can first observe that the overall performance of the SOTA methods is much worse than their performance on in-the-lab datasets like the CASIA-B [1]} and OU-ISIR series [2]}, [3]}.
This reflects that there is a huge gap between the in-the-lab research and the in-the-wild application that is much more challenging.
Meanwhile, the performance of the SOTA model-free methods varies significantly.
For example, the GEI-based method, i.e., GEINet obtains the worst results, which indicates that the GEIs discard too much useful information for gait recognition.
Moreover, the methods considering the order of the frames in sequences, i.e., GaitPart, GLN, GaitGL, and CSTL, obtain lower accuracy.
It means that the temporal information in the wild scene is hard to learn, because people may stop then continue to walk at varying speeds and routes in unconstrained scenarios.
On the contrary, the methods considering frames as an unordered set, i.e., GaitSet, obtain better results.
